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IDEC Image Segmentation

Advanced Image Processing Lecture based on Deep Learning in 한국과학기술원 반도체설계교육센터 (KAIST IDEC)

Pneumonia Classification and Pet Data Image Segmentation with U-Net
In 2021 Advanced Image Processing Lecture with KAIST IDEC

DataSet

Application

Model Loss Function Optimizer Epoch Total Loss Accuracy
(Classification)
(=F1-Score)
Dataset Result
U-Net Sparse Categorical Crossentropy RMSProp 200 0.0269
(Train)
. Pet Data Result_01
(Segmentation)
U-Net Sparse Categorical Crossentropy RMSProp 1000 0.0041
(Train)
. Pet Data Result_02
(Segmentation)
U-Net
Leaky ReLU
Sparse Categorical Crossentropy RMSProp 1000 0.0048
(Train)
. Pet Data Result_03
(Segmentation)
FCN Binary Crossentropy Adam 200 1.0335
(Test)
0.8650
(Test)
Pneumonia Data Result_04
(Classification)
FCN MSE Adam 200 0.1464
(Test)
0.8500
(Test)
Pneumonia Data Result_05
(Classification)
FCN Binary Crossentropy Nadam 200 1.6821
(Test)
0.7500
(Test)
Pneumonia Data Result_06
(Classification)
FCN Binary Crossentropy AdaDelta 200 0.3628
(Test)
0.8600
(Test)
Pneumonia Data Result_06
(Classification)

Result_01

Result_02

Result_03

Sample Dataset

Sample Pet Train Raw Data (each different size)


Siamese_203

yorkshire_terrier_67


Sample Pet Train Scaling Data (160 x 160 px)


Siamese_203

yorkshire_terrier_67

Sample Pneumonia Train Scaling Data (180 x 180 px)


Pneumonia

Reference

https://keras.io/examples/vision/